CTmax Data
ctmax_temp_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species)) +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ctmax_lat_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = lat, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species)) +
labs(x = "Latitude",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ctmax_elev_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = elevation, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species)) +
labs(x = "Elevation (m)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ggpubr::ggarrange(ctmax_temp_plot, ctmax_lat_plot, ctmax_elev_plot, common.legend = T, legend = "right", nrow = 1)

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = ctmax)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
filter(str_detect(species, pattern = "skisto") |
str_detect(species, pattern = "lepto")) %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
group_by(collection_date, species, collection_temp) %>%
summarise(mean_ctmax = mean(ctmax),
ctmax_sd = sd(ctmax),
ctmax_n = n(),
ctmax_se = ctmax_sd / sqrt(ctmax_n)) %>%
ggplot(aes(x = collection_temp, y = mean_ctmax, colour = species)) +
geom_smooth(method = "lm", se=F, linewidth = 2) +
geom_point(size = 2) +
geom_errorbar(aes(ymin = mean_ctmax - ctmax_se,
ymax = mean_ctmax + ctmax_se),
width = 0.3, linewidth = 1) +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = size)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "Prosome Length (mm)") +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = mean_egg)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "Prosome Length (mm)") +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
select(elevation, collection_temp) %>%
distinct() %>%
ggplot(aes(x = elevation, y = collection_temp)) +
geom_point(size = 3) +
labs(x = "Elevation (m)",
y = "Collection Temp. (°C)") +
theme_matt()

ggplot(ctmax_data, aes(x = size, y = ctmax, colour = species)) +
facet_wrap(.~species) +
geom_point() +
theme_matt() +
theme(legend.position = "none")

ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) +
geom_smooth(method = "lm", formula = y ~ exp(x)) +
geom_point()+
labs(x = "Prosome Length (mm)",
y = "Total Egg Volume (mm^3)") +
theme_matt()

ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) +
facet_wrap(.~species) +
geom_point()+
#geom_smooth() +
labs(x = "Prosome Length (mm)",
y = "Total Egg Volume (mm^3)") +
theme_matt()

model_data = ctmax_data %>%
mutate("genus" = str_split_fixed(species, pattern = "_", n = 2)[,1],
genus = tools::toTitleCase(genus),
"doy" = yday(collection_date)) %>%
select(site, collection_date, doy, collection_temp, lat, elevation, species, genus, sample_id, fecundity, size, ctmax) %>%
filter(genus != "MH")
ctmax_temp.model = lm(data = model_data,
ctmax ~ genus + collection_temp + lat + elevation)
ctmax_resids = residuals(ctmax_temp.model)
performance::check_model(ctmax_temp.model)
## Package `see` required for model diagnostic plots.
## There are binary versions available but the source versions are later:
## binary source needs_compilation
## correlation 0.8.4 0.8.5 FALSE
## effectsize 0.8.6 0.8.9 FALSE
## modelbased 0.8.6 0.8.8 FALSE
## parameters 0.21.2 0.22.2 FALSE
## see 0.8.0 0.9.0 FALSE
##
## $VIF
## # Check for Multicollinearity
##
## $QQ
## x y
## 151 34.75347 -3.384080681
## 152 34.75347 -3.150074640
## 153 34.75347 -2.698165779
## 154 34.75347 -2.443853787
## 155 34.75347 -2.058757124
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## 90 36.05428 -1.769221895
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## 148 36.69657 -0.817341924
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## 83 37.00493 -0.686527026
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## 107 37.07192 -0.432729371
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## 27 39.06764 1.952448424
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##
## $NORM
## x y curve
## 1 -2.432753 0.02135971 0.002806152
## 2 -2.428775 0.02150828 0.002854642
## 3 -2.424797 0.02165132 0.002903889
## 4 -2.420819 0.02178957 0.002953902
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## 264 -1.386553 0.13964572 0.096703387
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## 266 -1.378597 0.14071696 0.098606532
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## 270 -1.362686 0.14258280 0.102491435
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## 300 -1.243347 0.14618109 0.135002500
## 301 -1.239369 0.14608095 0.136188850
## 302 -1.235391 0.14597518 0.137381773
## 303 -1.231413 0.14586201 0.138581260
## 304 -1.227435 0.14574438 0.139787301
## 305 -1.223457 0.14562337 0.140999884
## 306 -1.219479 0.14549887 0.142218998
## 307 -1.215502 0.14537161 0.143444632
## 308 -1.211524 0.14524325 0.144676771
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## 311 -1.199590 0.14485732 0.148412087
## 312 -1.195612 0.14473099 0.149670108
## 313 -1.191634 0.14460733 0.150934562
## 314 -1.187656 0.14448789 0.152205431
## 315 -1.183678 0.14437214 0.153482697
## 316 -1.179700 0.14426037 0.154766342
## 317 -1.175722 0.14415682 0.156056348
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## 319 -1.167766 0.14396763 0.158655361
## 320 -1.163788 0.14388525 0.159964327
## 321 -1.159810 0.14381290 0.161279570
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## 323 -1.151854 0.14369331 0.163928800
## 324 -1.147876 0.14365302 0.165262739
## 325 -1.143898 0.14362270 0.166602861
## 326 -1.139921 0.14360257 0.167949142
## 327 -1.135943 0.14359794 0.169301555
## 328 -1.131965 0.14360766 0.170660073
## 329 -1.127987 0.14362922 0.172024670
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## 331 -1.120031 0.14371981 0.174771983
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## 333 -1.112075 0.14387047 0.177543259
## [ reached 'max' / getOption("max.print") -- omitted 691 rows ]
##
## $NCV
## x y
## 1 37.20461 4.117135e-01
## 2 37.20461 4.393335e-01
## 3 37.20461 1.087061e+00
## 4 37.20461 4.870608e-01
## 5 37.20461 5.271649e-01
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## 16 37.20461 1.162274e-01
## 17 38.23965 -1.938197e-01
## 18 38.23965 -6.875697e-01
## 19 38.23965 -1.037570e+00
## 20 38.23965 7.186803e-01
## 21 38.23965 -2.431252e-01
## 22 38.23965 4.159699e-02
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## 24 38.23965 2.811803e-01
## 25 38.23965 -3.259625e-01
## 26 38.23965 -1.326774e+00
## 27 39.06764 -6.405541e-01
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## 58 37.42623 3.832894e-01
## 59 37.42623 1.469599e+00
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## 120 37.45015 6.540201e-01
## 121 37.45015 1.071451e-01
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## 128 37.53963 -9.171453e-02
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## 135 37.53963 2.624521e-01
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## 150 36.69657 -1.268540e+00
## 151 34.75347 -6.985463e-01
## 152 34.75347 -2.511557e-01
## 153 34.75347 9.550288e-03
## 154 34.75347 7.391041e-02
## 155 34.75347 6.059045e-01
## 156 36.03326 -5.931533e-01
## 157 36.03326 -2.124241e-01
## 158 36.03326 -1.113825e-01
## 159 35.11304 -4.922109e-01
## 160 37.05557 -9.156915e-01
## 161 35.77579 -1.305547e+00
## 162 37.05557 -8.125169e-01
## 163 37.05557 -1.293072e+00
## 164 37.05557 -7.639058e-01
## 165 37.68333 -9.114237e-15
## 166 37.21343 2.142248e-02
## 167 37.21343 5.834490e-01
## 168 37.21343 7.657407e-01
## 169 37.21343 1.268518e-01
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## 171 37.21343 -3.089121e-01
## 172 37.21343 5.240740e-01
## 173 36.78676 -5.284303e-01
## 174 36.78676 5.674030e-01
## 175 36.78676 8.302801e-02
## 176 36.78676 3.104586e-01
##
## $HOMOGENEITY
## x y
## 1 37.20461 0.73632675
## 2 37.20461 0.76062424
## 3 37.20461 1.19646478
## 4 37.20461 0.80087471
## 5 37.20461 0.83319425
## 6 37.20461 0.86803907
## 7 37.20461 0.88185075
## 8 37.20461 0.95407714
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## 10 37.20461 0.54486357
## 11 37.20461 0.43888877
## 12 37.20461 0.38654010
## 13 37.20461 0.83113373
## 14 37.20461 0.89471964
## 15 37.20461 0.75016887
## 16 37.20461 0.39122564
## 17 38.23965 0.50680345
## 18 38.23965 0.95455068
## 19 38.23965 1.17259833
## 20 38.23965 0.97590718
## 21 38.23965 0.56761734
## 22 38.23965 0.23478568
## 23 38.23965 1.00379852
## 24 38.23965 0.61042611
## 25 38.23965 0.65724074
## 26 38.23965 1.32598664
## 27 39.06764 0.92652782
## 28 39.06764 1.40408620
## 29 39.06764 0.99552759
## 30 39.06764 0.69392356
## 31 38.20904 0.41558596
## 32 38.20904 0.51957678
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## 105 37.20849 1.55205619
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## 107 37.07192 1.04212293
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## 153 34.75347 0.11550052
## 154 34.75347 0.32131316
## 155 34.75347 0.91997873
## 156 36.03326 0.89200673
## 157 36.03326 0.53380986
## 158 36.03326 0.38653880
## 159 35.11304 0.82921471
## 160 37.05557 1.10145538
## 161 35.77579 1.35353500
## 162 37.05557 1.03754877
## 163 37.05557 1.30889179
## 164 37.05557 1.00603298
## 165 37.68333 NaN
## 166 37.21343 0.16844952
## 167 37.21343 0.87909610
## 168 37.21343 1.00710734
## 169 37.21343 0.40990496
## 170 37.21343 0.51588489
## 171 37.21343 0.63966469
## 172 37.21343 0.83316537
## 173 36.78676 0.84127597
## 174 36.78676 0.87174694
## 175 36.78676 0.33347011
## 176 36.78676 0.64483178
##
## $OUTLIERS
## OK: No outliers detected.
## - Based on the following method and threshold: cook (0.842).
## - For variable: (Whole model)
##
##
## $INFLUENTIAL
## Hat Cooks_Distance Predicted Residuals Std_Residuals Index Influential
## 1 0.007495498 3.699977e-04 37.20461 0.411713533 0.542177090 1 OK
## 2 0.007495498 4.213058e-04 37.20461 0.439333482 0.578549234 2 OK
## 3 0.007495498 2.579386e-03 37.20461 1.087060755 1.431527967 3 OK
## 4 0.007495498 5.178155e-04 37.20461 0.487060755 0.641400298 4 OK
## 5 0.007495498 6.065991e-04 37.20461 0.527164922 0.694212651 5 OK
## 6 0.007495498 7.146178e-04 37.20461 0.572179803 0.753491823 6 OK
## 7 0.007495498 7.611971e-04 37.20461 0.590532977 0.777660741 7 OK
## 8 0.007495498 1.042919e-03 37.20461 0.691227422 0.910263186 8 OK
## 9 0.007495498 8.425063e-04 37.20461 -0.621272578 -0.818141090 9 OK
## 10 0.007495498 1.109348e-04 37.20461 -0.225439245 -0.296876309 10 OK
## 11 0.007495498 4.670191e-05 37.20461 -0.146272578 -0.192623352 11 OK
## 12 0.007495498 2.809926e-05 37.20461 -0.113460078 -0.149413245 12 OK
## 13 0.007495498 6.006208e-04 37.20461 0.524560755 0.690783278 13 OK
## 14 0.007495498 8.066119e-04 37.20461 0.607894088 0.800523232 14 OK
## 15 0.007495498 3.986143e-04 37.20461 0.427338533 0.562753331 15 OK
## 16 0.007495498 2.948669e-05 37.20461 0.116227422 0.153057503 16 OK
## 17 0.019918979 2.234663e-04 38.23965 -0.193819674 -0.256849741 17 OK
## 18 0.019918979 2.812223e-03 38.23965 -0.687569674 -0.911167009 18 OK
## 19 0.019918979 6.403994e-03 38.23965 -1.037569674 -1.374986844 19 OK
## 20 0.019918979 3.072472e-03 38.23965 0.718680326 0.952394830 20 OK
## 21 0.019918979 3.516223e-04 38.23965 -0.243125230 -0.322189440 21 OK
## 22 0.019918979 1.029296e-05 38.23965 0.041596992 0.055124315 22 OK
## 23 0.019918979 3.439062e-03 38.23965 0.760346992 1.007611477 23 OK
## 24 0.019918979 4.703122e-04 38.23965 0.281180326 0.372620035 24 OK
## 25 0.019918979 6.320505e-04 38.23965 -0.325962531 -0.431965393 25 OK
## 26 0.019918979 1.047154e-02 38.23965 -1.326774220 -1.758240572 26 OK
## 27 0.041698375 5.344407e-03 39.06764 -0.640554051 -0.858453810 27 OK
## 28 0.041698375 2.818647e-02 39.06764 -1.471046475 -1.971458068 28 OK
## 29 0.041698375 7.123261e-03 39.06764 -0.739512384 -0.991075184 29 OK
## 30 0.041698375 1.681559e-03 39.06764 -0.359304051 -0.481529906 30 OK
## 31 0.032360064 1.662600e-04 38.20904 0.129498985 0.172711691 31 OK
## 32 0.032360064 4.062031e-04 38.20904 0.202415652 0.269960027 32 OK
## 33 0.032360064 7.802732e-04 38.20904 0.280540652 0.374154672 33 OK
## 34 0.032360064 3.285955e-03 38.20904 -0.575709348 -0.767818642 34 OK
## 35 0.032360064 2.129038e-04 38.20904 -0.146542681 -0.195442723 35 OK
## 36 0.032360064 1.859317e-02 38.20904 -1.369459348 -1.826436239 36 OK
## 37 0.032360064 3.381771e-03 38.20904 -0.584042681 -0.778932737 37 OK
## 38 0.032360064 7.881848e-04 38.20904 -0.281959348 -0.376046775 38 OK
## 39 0.032360064 2.777420e-04 38.20904 -0.167376015 -0.223227962 39 OK
## 40 0.021648581 1.125658e-05 38.41408 0.041653052 0.055247376 40 OK
## 41 0.021648581 5.476709e-05 38.41408 0.091876266 0.121861961 41 OK
## 42 0.021648581 7.650886e-03 38.41408 1.085923885 1.440337304 42 OK
## 43 0.021648581 1.235710e-04 38.41408 0.138007219 0.183048691 43 OK
## 44 0.021648581 2.143366e-04 38.41408 0.181757219 0.241077396 44 OK
## 45 0.021648581 3.178593e-04 38.41408 0.221340552 0.293579558 45 OK
## 46 0.021648581 5.227112e-04 38.41408 0.283840552 0.376477708 46 OK
## 47 0.021648581 8.555622e-05 38.41408 -0.114833690 -0.152312008 47 OK
## 48 0.021648581 1.589266e-05 38.41408 -0.049492781 -0.065645760 48 OK
## 49 0.021648581 4.310470e-03 38.41408 0.815090552 1.081111986 49 OK
## 50 0.021648581 9.227463e-05 38.41408 0.119257219 0.158179246 50 OK
## 51 0.021648581 1.836478e-04 38.41408 -0.168242781 -0.223152246 51 OK
## 52 0.021648581 2.344412e-04 38.41408 0.190090552 0.252130483 52 OK
## 53 0.021648581 5.355781e-04 38.41408 0.287312774 0.381083161 53 OK
## 54 0.016593757 2.125268e-03 37.42623 0.657098946 0.869313758 54 OK
## 55 0.016593757 4.430675e-03 37.42623 0.948765613 1.255176264 55 OK
## 56 0.016593757 3.396652e-03 37.42623 0.830710057 1.098993821 56 OK
## 57 0.016593757 1.822451e-03 37.42623 0.608487835 0.805003340 57 OK
## 58 0.016593757 7.231123e-04 37.42623 0.383289422 0.507075487 58 OK
## 59 0.016593757 1.063040e-02 37.42623 1.469598946 1.944216453 59 OK
## 60 0.016593757 1.104788e-03 37.42623 0.473765613 0.626771611 60 OK
## 61 0.038737683 9.806386e-04 36.45743 -0.285557093 -0.382106351 61 OK
## 62 0.038737683 2.902276e-04 36.45743 -0.155348760 -0.207873484 62 OK
## 63 0.038737683 4.715452e-02 36.45743 -1.980159366 -2.649667923 63 OK
## 64 0.038737683 5.219849e-03 36.45743 -0.658820982 -0.881573904 64 OK
## 65 0.038737683 2.135076e-02 36.45743 -1.332432093 -1.782938604 65 OK
## 66 0.011840999 4.412634e-05 37.48529 0.112627637 0.148642780 66 OK
## 67 0.011840999 1.038581e-04 37.48529 -0.172789030 -0.228042090 67 OK
## 68 0.011840999 3.293618e-03 37.48529 0.973044304 1.284196441 68 OK
## 69 0.011840999 5.189430e-04 37.48529 0.386238748 0.509747011 69 OK
## 70 0.011840999 2.905717e-04 37.48529 0.289016526 0.381435863 70 OK
## 71 0.011840999 2.190069e-04 37.48529 -0.250914030 -0.331149263 71 OK
## 72 0.011840999 1.294706e-05 37.48529 0.061007267 0.080515671 72 OK
## 73 0.011840999 2.090039e-03 37.48529 0.775127637 1.022991603 73 OK
## 74 0.011840999 2.501757e-03 37.48529 0.848044304 1.119224965 74 OK
## 75 0.011840999 9.648061e-05 37.48529 -0.166539030 -0.219793516 75 OK
## 76 0.006984488 1.549269e-03 37.43831 -0.873203735 -1.149608142 76 OK
## 77 0.006984488 1.685761e-05 37.43831 -0.091085680 -0.119917992 77 OK
## 78 0.006984488 1.627671e-03 37.43831 0.895025431 1.178337291 78 OK
## 79 0.010647368 2.492956e-03 37.54070 0.893821174 1.178928168 79 OK
## 80 0.010647368 3.104788e-03 37.54070 0.997491809 1.315667188 80 OK
## 81 0.010647368 7.896435e-04 37.54070 0.503047364 0.663507114 81 OK
## 82 0.010214968 5.110922e-06 37.37467 -0.041336661 -0.054510129 82 OK
## 83 0.014269208 1.396215e-05 37.00493 0.057570229 0.076073091 83 OK
## 84 0.010267274 1.499001e-04 37.37606 -0.223282528 -0.294447613 84 OK
## 85 0.010267274 3.103343e-03 37.37606 -1.015941258 -1.339744229 85 OK
## 86 0.010267274 7.191190e-03 37.37606 -1.546514851 -2.039423373 86 OK
## 87 0.010267274 2.601779e-03 37.37606 -0.930226972 -1.226710903 87 OK
## 88 0.166078816 1.835137e-01 34.98831 1.636686530 2.351339112 88 OK
## 89 0.166078816 1.216591e-02 34.98831 0.421408753 0.605415187 89 OK
## 90 0.055826006 3.323015e-03 36.05428 0.430093917 0.580696951 90 OK
## 91 0.055826006 1.090282e-02 36.05428 0.779052250 1.051847628 91 OK
## 92 0.055826006 1.733421e-02 36.05428 -0.982311386 -1.326280620 92 OK
## 93 0.055826006 5.569947e-03 36.05428 0.556830028 0.751811376 93 OK
## 94 0.055826006 6.554730e-03 36.05428 0.604052250 0.815569079 94 OK
## 95 0.055826006 1.063168e-01 36.05428 -2.432753305 -3.284613829 95 OK
## 96 0.055826006 2.140399e-02 36.05428 1.091552250 1.473773608 96 OK
## 97 0.055826006 3.680921e-03 36.05428 0.452663361 0.611169383 97 OK
## 98 0.055826006 7.931496e-03 36.05428 0.664468917 0.897141436 98 OK
## 99 0.055826006 2.251855e-03 36.05428 0.354052250 0.478028296 99 OK
## 100 0.055826006 3.625348e-02 36.05428 -1.420600528 -1.918042461 100 OK
## 101 0.055826006 9.767762e-03 36.05428 0.737385583 0.995590831 101 OK
## 102 0.055826006 1.079985e-05 36.05428 -0.024519178 -0.033104891 102 OK
## 103 0.055826006 1.943299e-03 36.05428 -0.328902295 -0.444071754 103 OK
## 104 0.055826006 1.123785e-03 36.05428 -0.250114417 -0.337695265 104 OK
## 105 0.025398181 2.520310e-02 37.20849 -1.812659472 -2.408878408 105 OK
## 106 0.025398181 1.726221e-02 37.20849 -1.500159472 -1.993591084 106 OK
## 107 0.019374605 3.883777e-03 37.07192 0.819742043 1.086020203 107 OK
## 108 0.019374605 1.284126e-02 37.07192 1.490575376 1.974761435 108 OK
## 109 0.019374605 4.670152e-03 37.07192 0.898908709 1.190902709 109 OK
## 110 0.019374605 2.153188e-03 37.07192 0.610367043 0.808633576 110 OK
## 111 0.019374605 8.895004e-03 37.07192 1.240575376 1.643553522 111 OK
## 112 0.019374605 1.450646e-03 37.07192 0.500992043 0.663730115 112 OK
## 113 0.019374605 5.280587e-03 37.07192 0.955853154 1.266344511 113 OK
## 114 0.012887725 2.617338e-04 37.45015 -0.262646541 -0.346817235 114 OK
## 115 0.012887725 1.173158e-04 37.45015 -0.175840985 -0.232192985 115 OK
## 116 0.012887725 8.160044e-03 37.45015 1.466520126 1.936497827 116 OK
## 117 0.012887725 5.251128e-04 37.45015 -0.372021541 -0.491243790 117 OK
## 118 0.012887725 7.919500e-06 37.45015 0.045686792 0.060328101 118 OK
## 119 0.012887725 1.357096e-03 37.45015 -0.598063208 -0.789725338 119 OK
## 120 0.012887725 1.622926e-03 37.45015 0.654020126 0.863614846 120 OK
## 121 0.012887725 4.355733e-05 37.45015 0.107145126 0.141482070 121 OK
## 122 0.012887725 2.315641e-05 37.45015 -0.078122731 -0.103158829 122 OK
## 123 0.012887725 1.233632e-07 37.45015 -0.005702096 -0.007529455 123 OK
## 124 0.012887725 2.137507e-04 37.45015 0.237353459 0.313418445 124 OK
## 125 0.021063713 2.265063e-04 37.53963 0.189535469 0.251319128 125 OK
## 126 0.021063713 6.747506e-04 37.53963 -0.327131197 -0.433767503 126 OK
## 127 0.021063713 2.009433e-04 37.53963 -0.178520086 -0.236713015 127 OK
## 128 0.021063713 5.303660e-05 37.53963 -0.091714531 -0.121611095 128 OK
## 129 0.021063713 4.511470e-03 37.53963 -0.845881197 -1.121616580 129 OK
## 130 0.021063713 9.656580e-03 37.53963 -1.237547864 -1.640956445 130 OK
## 131 0.021063713 3.701489e-03 37.53963 -0.766193697 -1.015953017 131 OK
## 132 0.021063713 1.028420e-02 37.53963 -1.277131197 -1.693442921 132 OK
## 133 0.021063713 3.876845e-04 37.53963 -0.247964531 -0.328794552 133 OK
## 134 0.021063713 1.271942e-02 37.53963 -1.420313016 -1.883298307 134 OK
## 135 0.021063713 4.343097e-04 37.53963 0.262452136 0.348004741 135 OK
## 136 0.021063713 7.091607e-04 37.53963 0.335368803 0.444690354 136 OK
## 137 0.021063713 9.572090e-04 37.53963 -0.389631197 -0.516640886 137 OK
## 138 0.021063713 2.152447e-03 37.53963 -0.584274055 -0.774732278 138 OK
## 139 0.021063713 1.267715e-05 37.53963 -0.044839531 -0.059456058 139 OK
## 140 0.021063713 3.381067e-02 37.53963 -2.315672864 -3.070522298 140 OK
## 141 0.018822483 2.205836e-03 37.93953 0.627132215 0.830610811 141 OK
## 142 0.018822483 3.337473e-03 37.93953 0.771403049 1.021691592 142 OK
## [ reached 'max' / getOption("max.print") -- omitted 34 rows ]
##
## $PP_CHECK

##
## attr(,"class")
## [1] "check_model" "see_check_model"
## attr(,"panel")
## [1] TRUE
## attr(,"dot_size")
## [1] 2
## attr(,"line_size")
## [1] 0.8
## attr(,"base_size")
## [1] 10
## attr(,"axis_title_size")
## [1] 10
## attr(,"title_size")
## [1] 12
## attr(,"check")
## [1] "all"
## attr(,"alpha")
## [1] 0.2
## attr(,"dot_alpha")
## [1] 0.8
## attr(,"show_dots")
## [1] TRUE
## attr(,"detrend")
## [1] TRUE
## attr(,"colors")
## [1] "#3aaf85" "#1b6ca8" "#cd201f"
## attr(,"theme")
## [1] "see::theme_lucid"
## attr(,"model_info")
## attr(,"model_info")$is_binomial
## [1] FALSE
##
## attr(,"model_info")$is_bernoulli
## [1] FALSE
##
## attr(,"model_info")$is_count
## [1] FALSE
##
## attr(,"model_info")$is_poisson
## [1] FALSE
##
## attr(,"model_info")$is_negbin
## [1] FALSE
##
## attr(,"model_info")$is_beta
## [1] FALSE
##
## attr(,"model_info")$is_betabinomial
## [1] FALSE
##
## attr(,"model_info")$is_orderedbeta
## [1] FALSE
##
## attr(,"model_info")$is_dirichlet
## [1] FALSE
##
## attr(,"model_info")$is_exponential
## [1] FALSE
##
## attr(,"model_info")$is_logit
## [1] FALSE
##
## attr(,"model_info")$is_probit
## [1] FALSE
##
## attr(,"model_info")$is_censored
## [1] FALSE
##
## attr(,"model_info")$is_truncated
## [1] FALSE
##
## attr(,"model_info")$is_survival
## [1] FALSE
##
## attr(,"model_info")$is_linear
## [1] TRUE
##
## attr(,"model_info")$is_tweedie
## [1] FALSE
##
## attr(,"model_info")$is_zeroinf
## [1] FALSE
##
## attr(,"model_info")$is_zero_inflated
## [1] FALSE
##
## attr(,"model_info")$is_dispersion
## [1] FALSE
##
## attr(,"model_info")$is_hurdle
## [1] FALSE
##
## attr(,"model_info")$is_ordinal
## [1] FALSE
##
## attr(,"model_info")$is_cumulative
## [1] FALSE
##
## attr(,"model_info")$is_multinomial
## [1] FALSE
##
## attr(,"model_info")$is_categorical
## [1] FALSE
##
## attr(,"model_info")$is_mixed
## [1] FALSE
##
## attr(,"model_info")$is_multivariate
## [1] FALSE
##
## attr(,"model_info")$is_trial
## [1] FALSE
##
## attr(,"model_info")$is_bayesian
## [1] FALSE
##
## attr(,"model_info")$is_gam
## [1] FALSE
##
## attr(,"model_info")$is_anova
## [1] FALSE
##
## attr(,"model_info")$is_timeseries
## [1] FALSE
##
## attr(,"model_info")$is_ttest
## [1] FALSE
##
## attr(,"model_info")$is_correlation
## [1] FALSE
##
## attr(,"model_info")$is_onewaytest
## [1] FALSE
##
## attr(,"model_info")$is_chi2test
## [1] FALSE
##
## attr(,"model_info")$is_ranktest
## [1] FALSE
##
## attr(,"model_info")$is_levenetest
## [1] FALSE
##
## attr(,"model_info")$is_variancetest
## [1] FALSE
##
## attr(,"model_info")$is_xtab
## [1] FALSE
##
## attr(,"model_info")$is_proptest
## [1] FALSE
##
## attr(,"model_info")$is_binomtest
## [1] FALSE
##
## attr(,"model_info")$is_ftest
## [1] FALSE
##
## attr(,"model_info")$is_meta
## [1] FALSE
##
## attr(,"model_info")$link_function
## [1] "identity"
##
## attr(,"model_info")$family
## [1] "gaussian"
##
## attr(,"model_info")$n_obs
## [1] 176
##
## attr(,"model_info")$n_grouplevels
## NULL
##
## attr(,"bandwidth")
## [1] "nrd"
## attr(,"type")
## [1] "density"
## attr(,"model_class")
## [1] "lm"
emmeans::emmeans(ctmax_temp.model, specs = "genus") %>%
data.frame() %>%
mutate(genus = fct_reorder(genus, .x = emmean, .desc = T)) %>%
ggplot(aes(genus, y = emmean)) +
geom_point(size = 4) +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE),
width = 0.2, linewidth = 1) +
labs(x = "") +
theme_matt() +
theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))

ctmax_data %>%
mutate(group_id = paste(site, species)) %>%
ggplot(aes(x = fecundity, y = site, fill = site)) +
geom_density_ridges(bandwidth = 2,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "E", direction = -1) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(group_id = paste(site, species)) %>%
ggplot(aes(x = size, y = site, fill = site, group = group_id)) +
geom_density_ridges(bandwidth = 0.02,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "E", direction = -1) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(group_id = paste(site, species)) %>%
ggplot(aes(x = ctmax, y = site, fill = site, group = group_id)) +
geom_density_ridges(bandwidth = 0.3,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "E", direction = -1) +
labs(x = "CTmax (°C)") +
theme_matt() +
theme(legend.position = "none")

---
title: Diaptomid Thermal Limits
date: "`r Sys.Date()`"
output: 
  html_document:
          code_folding: hide
          code_download: true
          toc: true
          toc_float: true
  github_document:
          html_preview: false
          toc: true
          toc_depth: 3
---

```{r setup, include=T, message = F, warning = F, echo = F}
knitr::opts_chunk$set(
  echo = knitr::is_html_output(),
  fig.align = "center",
  fig.path = "../Figures/markdown/",
  dev = c("png", "pdf"),
  message = FALSE,
  warning = FALSE,
  collapse = T
)

theme_matt = function(base_size = 18,
                      dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  ggpubr::theme_pubr(base_family="sans") %+replace% 
    theme(
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

theme_matt_facets = function(base_size = 18,
                             dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  theme_bw(base_family="sans") %+replace% 
    theme(
      panel.grid = element_blank(),
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      strip.text.x = element_text(size = base_size),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}
```

## Site Map

```{r sampled-sites, fig.width=10, fig.height=6}
coords = site_data %>%
  dplyr::select(site, long, lat, collection_temp) %>%
  drop_na(collection_temp) %>% 
  distinct()

map_data("world") %>% 
  filter(region %in% c("USA", "Canada")) %>% 
  ggplot() + 
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = "lightgrey") + 
  coord_map(xlim = c(-110,-60),
            ylim = c(25, 55)) + 
  geom_point(data = coords,
             mapping = aes(x = long, y = lat, colour = collection_temp),
             size = 3) +
  scale_colour_viridis_c(option = "F") + 
  labs(x = "Longitude", 
       y = "Latitude",
       colour = "Temp.") + 
  theme_matt() + 
  theme(legend.position = "right")
```

## CTmax Data 

```{r fig.width=20, fig.height=6}
ctmax_temp_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species)) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_lat_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = lat, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species)) + 
  labs(x = "Latitude", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_elev_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = elevation, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species)) +
  labs(x = "Elevation (m)", 
       y = "CTmax (°C)") +
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggpubr::ggarrange(ctmax_temp_plot, ctmax_lat_plot, ctmax_elev_plot, common.legend = T, legend = "right", nrow = 1)
```


```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r fig.width=10, fig.height=6}
ctmax_data %>% 
  filter(str_detect(species, pattern = "skisto") | 
           str_detect(species, pattern = "lepto")) %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  group_by(collection_date, species, collection_temp) %>% 
  summarise(mean_ctmax = mean(ctmax),
            ctmax_sd = sd(ctmax),
            ctmax_n = n(), 
            ctmax_se = ctmax_sd / sqrt(ctmax_n)) %>% 
  ggplot(aes(x = collection_temp, y = mean_ctmax, colour = species)) + 
  geom_smooth(method = "lm", se=F, linewidth = 2) + 
  geom_point(size = 2) + 
  geom_errorbar(aes(ymin = mean_ctmax - ctmax_se, 
                    ymax = mean_ctmax + ctmax_se),
                width = 0.3, linewidth = 1) + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = size)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Prosome Length (mm)") + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = mean_egg)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Prosome Length (mm)") + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r}
ctmax_data %>% 
  select(elevation, collection_temp) %>% 
  distinct() %>% 
  ggplot(aes(x = elevation, y = collection_temp)) + 
  geom_point(size = 3) +
  labs(x = "Elevation (m)", 
       y = "Collection Temp. (°C)") + 
  theme_matt()
```

```{r}
ggplot(ctmax_data, aes(x = size, y = ctmax, colour = species)) + 
  facet_wrap(.~species) + 
  geom_point() + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r}
ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) + 
  geom_smooth(method = "lm", formula = y ~ exp(x)) + 
  geom_point()+
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt()

ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) + 
  facet_wrap(.~species) + 
  geom_point()+
  #geom_smooth() + 
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt()

```


```{r}
model_data = ctmax_data %>% 
  mutate("genus" = str_split_fixed(species, pattern = "_", n = 2)[,1],
         genus = tools::toTitleCase(genus),
         "doy" = yday(collection_date)) %>% 
  select(site, collection_date, doy, collection_temp, lat, elevation, species, genus, sample_id, fecundity, size, ctmax) %>% 
  filter(genus != "MH")

ctmax_temp.model = lm(data = model_data, 
                      ctmax ~ genus + collection_temp + lat + elevation)

ctmax_resids = residuals(ctmax_temp.model)

performance::check_model(ctmax_temp.model)

emmeans::emmeans(ctmax_temp.model, specs = "genus") %>% 
  data.frame() %>% 
  mutate(genus = fct_reorder(genus, .x = emmean, .desc = T)) %>% 
  ggplot(aes(genus, y = emmean)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), 
                width = 0.2, linewidth = 1) + 
  labs(x = "") + 
  theme_matt() + 
  theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))
```


```{r fecundity-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species)) %>% 
  ggplot(aes(x = fecundity, y = site, fill = site)) + 
  geom_density_ridges(bandwidth = 2,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "E", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r size-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species)) %>% 
  ggplot(aes(x = size, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.02,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "E", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r ctmax-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species)) %>% 
  ggplot(aes(x = ctmax, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.3,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "E", direction = -1) + 
  labs(x = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "none")
```
